code: def _slice_weight(
      weight: T,
      slice_: multidim_slice | list[tuple[multidim_slice, float]],
  ) -> T:
      # slice_ can be a tuple of slice, e.g., ([3:6], [2:4])
      # or tuple of slice -> float, e.g. {([3:6],): 0.6, ([2:4],): 0.3}
  
      if isinstance(slice_, list):
          # for weighted case, we get the corresponding masks. e.g.,
          # {([3:6],): 0.6, ([2:4],): 0.3} => [0, 0, 0.3, 0.9, 0.6, 0.6] (if the whole length is 6)
          # this mask is broadcasted and multiplied onto the weight
  
          masks = []
  
          # the accepted argument is list of tuple here
          # because slice can't be key of dict
          for sl, wt in slice_:
              # create a mask with weight w
              with torch.no_grad():
                  mask = zeros_like(weight)
                  mask[_eliminate_list_slice(weight.shape, sl)] = 1  # type: ignore
  
              # track gradients here
              masks.append(mask * wt)  # type: ignore
  
          masks = sum(masks)
  
          return masks * weight  # type: ignore
  
      else:
          # for unweighted case, we slice it directly.
  
          def _do_slice(arr, slice_):
              return arr[_eliminate_list_slice(arr.shape, slice_)]  # type: ignore
  
          # sometimes, we don't need slice.
          # this saves an op on computational graph, which will hopefully make training faster
  
          # Use a dummy array to check this. Otherwise it would be too complex.
          dummy_arr = np.zeros(weight.shape, dtype=bool)  # type: ignore
          no_effect = (
              cast(Any, _do_slice(dummy_arr, slice_)).shape
              == dummy_arr.shape
          )
  
          if no_effect:
              return weight
  
          return _do_slice(weight, slice_)
  
imports: from __future__ import annotations
  import operator
  from typing import Callable, Iterator, TypeVar, Any, Optional, Tuple, Union, List, Dict, Generic, cast
  import numpy
  import torch
label_desc: Use image node_id 3 for calling a global function with example usage: _slice_weight(weight, slice_) and returns: unknown, _do_slice, arr, weight